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RCollett

7/14/2010 10:52 PM EDT

I don't necessarily believe that rigid metrics increases throughput. Rather, ...

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RCollett

7/14/2010 10:13 PM EDT

Duane,

The answer really depends on team size. The impact of any ...

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How productive is your R&D organization?

Ron Collett

7/6/2010 11:21 PM EDT

In early June, the U.S. Labor Department lowered its estimation of first-quarter productivity-growth to 2.8 percent on an annualized basis. The revision came in large part because companies are still cautious about hiring new workers and are adding more hours to their existing labor forces instead.

There is always some debate over the accuracy of government labor statistics when so much of the economy is driven by services rather than the output of easily measurable items like steel or cars. And that raises fundamental questions in our industry, as executives strive to improve engineering productivity:

• How should we measure product-development productivity?
• And why is measurement important?

Productivity drives development throughput in your R&D organization — the higher the productivity, the greater the throughput. And throughput is a measure of how much product the engineering organization churns out during a given period of time.

There are three ways to boost R&D throughput:

• Add headcount
• Increase work-hours per week
• Raise utilization and productivity

The first two have downside: Raising R&D headcount increases cost, and more hours lead to workforce burnout and high turnover.

The only viable long-term strategies for sustaining high throughput are to increase engineering utilization and productivity.


Utilization

Increasing R&D utilization—the percentage of the engineering workforce’s effort spent on revenue-generating activities—is among the quickest and most effective ways to boost throughput. That’s because it essentially increases R&D resources without incurring additional cost.

Organizations struggling with low utilization find their engineers spend more than half their time on non-revenue-generating activities, such as sales, customer support, and product support — all of which should be handled by different groups. In large companies, that means millions of dollars a year are being squandered.

Engineering organizations in best-in-class companies, however, spend 73 percent of their engineering time on activities that generate revenue and create persistent value. By shrinking the amount of time engineers spend on projects that get canceled, non-core research, myriad internal initiatives, and so forth, companies can significantly raise their utilization rates and, in the process, reduce R&D spending and/or develop new revenue-generating products.

Productivity
Productivity — the second factor driving throughput — is the amount of engineering output per unit of labor expended to create that output. Productivity is a function of efficiency. Only by improving efficiency will productivity rise. Analysis of R&D efficiency compares the effort a particular set of engineering tasks should consume to what they actually consume. Reducing the effort needed to complete a set of tasks raises efficiency, which increases productivity, and this gives rise to higher throughput.

Boosting productivity requires a reliable measurement system—one yielding accurate baselines and fair comparisons. Additionally, a robust measurement system paves the way for managers to determine the absolute minimum staffing projects need to finish on time. At that point, the projects are “optimally understaffed,” which means the projects can be staffed to levels that assume the teams will meet an improved productivity level.

And there’s where best-in-class companies are pushing the productivity envelope.

About the author:
Ron Collett is founder and CEO of Numetrics Management Systems, Inc. (Cupertino, Calif.), which provides semiconductor and embedded systems companies with fact-based product-development planning software that lays the foundation for improved productivity.




Duane Benson

7/7/2010 12:35 PM EDT

I've always wondered how the "virtuoso syndrome" factors into productivity. Throughout my career, I've seen a lot of good engineers and as a group, they all tend to perform at a similar level. But I've run into a number of engineers that seem to be able to produce five or ten times as much work as everyone else. And I'm not talking about prima-donna types. These folks just have a much greater level of understanding or mental processing power than most people. If you've got one of those folks on your team, should you plan with that individual level of productivity in mind? Or, should you plan based on a standard engineer just in case the virtuoso leaves?

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RCollett

7/14/2010 10:13 PM EDT

Duane,

The answer really depends on team size. The impact of any particular individual on productivity depends on whether the design team is large or small. If small, a single individual can have a significant impact on productivity. If the team is large, the impact of one person is typically far less. So wrt project planning, if the team is large, the productivity should not be based on that "super individual," whereas if the team is small, then it would be reasonable to boost the productivity estimate accordingly. As I'm sure you agree, a reliable estimate of team productivity is critical to getting a reliable estimate of schedule and resource requirements, and is an integral element of what Numetrics refers to as "Fact-based Planning"

Kind rgds,

Ron

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KB3001

7/7/2010 12:49 PM EDT

I question the use of rigid metrics to increase throughput. Driving productivity too aggressively can lead to the loss of inventiveness which in turn reduces competitivity and hence throughput. I am all for using metrics to monitor progress and drive throughput but these should be subject to human judgment and common sense, rather than used slavishely. Remember evolution, things that could drive survival and fitness in the short term could be lethal for a species in the long term.

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RCollett

7/14/2010 10:52 PM EDT

I don't necessarily believe that rigid metrics increases throughput. Rather, metrics applied in the proper way will improve productivity and throughput, and it will improve on-time schedule performance.

Metrics provide a way to achieve a balanced project plan -- which is a plan whose staffing level is commensurate with the design complexity and the schedule constraints, as well as an aggressive but achievable team productivity target.

Here's an example of how metrics can achieve that. We can calculate the productivity assumed in a project plan(which Numetrics does with its Fact-based Planning software). Let's say the calculation reveals that in order for the team to finish the project on schedule it would need to double its productivity compared to the team's prior performance. That means the likelihood of hitting schedule is extremely low. This use of metrics (e.g. measuring the implied productivity in a project plan) provides a fact-based approach to identify project plans whose execution assumptions are wholly unrealistic. Facts, data and metrics are the best way to communicate (e.g. to mgmt.) that either the scope of the project needs revision (i.e. the spec), or the team size needs to be increased (e.g. to a point that the team must achieve very high but not unrealistic productivity), or the schedule needs to be lengthened. Otherwise the project is not likely to hit the target schedule.

Also, it turns out that a harmonized project plan results in higher productivity -- harmonized in terms of the the staffing level being commensurate with the design complexity and schedule constraint.

Kind rgds,

Ron

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